Overview

Dataset statistics

Number of variables15
Number of observations148248
Missing cells88239
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.3 MiB
Average record size in memory249.7 B

Variable types

Numeric3
TimeSeries10
Categorical2

Alerts

data_hora has a high cardinality: 148248 distinct valuesHigh cardinality
pressao is highly overall correlated with temperatura_orvalhoHigh correlation
radiacao is highly overall correlated with temperatura and 3 other fieldsHigh correlation
temperatura is highly overall correlated with radiacao and 5 other fieldsHigh correlation
temperatura_orvalho is highly overall correlated with pressaoHigh correlation
temp_max_hr_anterior is highly overall correlated with radiacao and 5 other fieldsHigh correlation
temp_min_hr_anterior is highly overall correlated with temperatura and 4 other fieldsHigh correlation
umidade_max_hr_anterior is highly overall correlated with temperatura and 4 other fieldsHigh correlation
umidade_min_hr_anterior is highly overall correlated with radiacao and 5 other fieldsHigh correlation
umidade is highly overall correlated with radiacao and 5 other fieldsHigh correlation
chuva has 7215 (4.9%) missing valuesMissing
pressao has 7056 (4.8%) missing valuesMissing
radiacao has 7099 (4.8%) missing valuesMissing
temperatura has 7056 (4.8%) missing valuesMissing
temperatura_orvalho has 7285 (4.9%) missing valuesMissing
temp_max_hr_anterior has 7470 (5.0%) missing valuesMissing
temp_min_hr_anterior has 7470 (5.0%) missing valuesMissing
umidade_max_hr_anterior has 7710 (5.2%) missing valuesMissing
umidade_min_hr_anterior has 7710 (5.2%) missing valuesMissing
umidade has 7284 (4.9%) missing valuesMissing
vento_direcao has 7442 (5.0%) missing valuesMissing
vento_velocidade has 7442 (5.0%) missing valuesMissing
pressao is non stationaryNon stationary
radiacao is non stationaryNon stationary
temperatura is non stationaryNon stationary
temperatura_orvalho is non stationaryNon stationary
temp_max_hr_anterior is non stationaryNon stationary
temp_min_hr_anterior is non stationaryNon stationary
umidade_max_hr_anterior is non stationaryNon stationary
umidade_min_hr_anterior is non stationaryNon stationary
umidade is non stationaryNon stationary
pressao is seasonalSeasonal
radiacao is seasonalSeasonal
temperatura is seasonalSeasonal
temperatura_orvalho is seasonalSeasonal
temp_max_hr_anterior is seasonalSeasonal
temp_min_hr_anterior is seasonalSeasonal
umidade_max_hr_anterior is seasonalSeasonal
umidade_min_hr_anterior is seasonalSeasonal
umidade is seasonalSeasonal
data_hora is uniformly distributedUniform
time is uniformly distributedUniform
data_hora has unique valuesUnique
time has unique valuesUnique
chuva has 129512 (87.4%) zerosZeros
vento_velocidade has 3827 (2.6%) zerosZeros

Reproduction

Analysis started2023-05-08 14:01:54.528434
Analysis finished2023-05-08 14:16:33.501404
Duration14 minutes and 38.97 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

chuva
Real number (ℝ)

MISSING  ZEROS 

Distinct164
Distinct (%)0.1%
Missing7215
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean0.17850716
Minimum0
Maximum74.8
Zeros129512
Zeros (%)87.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-08T14:16:33.668643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum74.8
Range74.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3041834
Coefficient of variation (CV)7.3060566
Kurtosis400.00234
Mean0.17850716
Median Absolute Deviation (MAD)0
Skewness15.914505
Sum25175.4
Variance1.7008943
MonotonicityNot monotonic
2023-05-08T14:16:33.930286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 129512
87.4%
0.2 3782
 
2.6%
0.4 1214
 
0.8%
0.6 797
 
0.5%
0.8 639
 
0.4%
1 499
 
0.3%
1.2 418
 
0.3%
1.4 344
 
0.2%
1.6 287
 
0.2%
1.8 275
 
0.2%
Other values (154) 3266
 
2.2%
(Missing) 7215
 
4.9%
ValueCountFrequency (%)
0 129512
87.4%
0.2 3782
 
2.6%
0.4 1214
 
0.8%
0.6 797
 
0.5%
0.8 639
 
0.4%
1 499
 
0.3%
1.2 418
 
0.3%
1.4 344
 
0.2%
1.6 287
 
0.2%
1.8 275
 
0.2%
ValueCountFrequency (%)
74.8 1
< 0.1%
64.4 1
< 0.1%
53.4 1
< 0.1%
48.4 1
< 0.1%
46.4 1
< 0.1%
46.2 1
< 0.1%
44 1
< 0.1%
41.8 1
< 0.1%
41.4 1
< 0.1%
40.6 2
< 0.1%

pressao
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct250
Distinct (%)0.2%
Missing7056
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean910.61991
Minimum897.7
Maximum923.3
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-05-08T14:16:34.199098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum897.7
5-th percentile905.4
Q1908.4
median910.4
Q3912.8
95-th percentile916.3
Maximum923.3
Range25.6
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation3.3151153
Coefficient of variation (CV)0.0036405039
Kurtosis-0.0063468342
Mean910.61991
Median Absolute Deviation (MAD)2.2
Skewness0.1412564
Sum1.2857225 × 108
Variance10.98999
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.493980372 × 10-30
2023-05-08T14:16:34.519148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
910 1885
 
1.3%
909.7 1850
 
1.2%
909.8 1800
 
1.2%
910.1 1789
 
1.2%
910.7 1783
 
1.2%
910.8 1781
 
1.2%
910.5 1778
 
1.2%
909.5 1776
 
1.2%
909.3 1776
 
1.2%
909.1 1771
 
1.2%
Other values (240) 123203
83.1%
(Missing) 7056
 
4.8%
ValueCountFrequency (%)
897.7 1
 
< 0.1%
898.2 2
 
< 0.1%
898.3 1
 
< 0.1%
898.4 2
 
< 0.1%
898.5 3
< 0.1%
898.6 2
 
< 0.1%
898.8 2
 
< 0.1%
898.9 1
 
< 0.1%
899 5
< 0.1%
899.1 1
 
< 0.1%
ValueCountFrequency (%)
923.3 1
 
< 0.1%
923.1 1
 
< 0.1%
923 1
 
< 0.1%
922.9 2
< 0.1%
922.7 1
 
< 0.1%
922.6 1
 
< 0.1%
922.5 2
< 0.1%
922.4 4
< 0.1%
922.3 2
< 0.1%
922.2 2
< 0.1%
2023-05-08T14:16:36.461667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

radiacao
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct61390
Distinct (%)43.5%
Missing7099
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean495705.76
Minimum-3538
Maximum4385403
Zeros1209
Zeros (%)0.8%
Memory size1.1 MiB
2023-05-08T14:16:37.153853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-3538
5-th percentile-3279
Q1-3.54
median278
Q3520021
95-th percentile2733951.4
Maximum4385403
Range4388941
Interquartile range (IQR)520024.54

Descriptive statistics

Standard deviation927767.25
Coefficient of variation (CV)1.8716088
Kurtosis2.1949135
Mean495705.76
Median Absolute Deviation (MAD)2235
Skewness1.841436
Sum6.9968373 × 1010
Variance8.6075207 × 1011
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.424938881 × 10-26
2023-05-08T14:16:37.713700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.54 21888
 
14.8%
-4 9989
 
6.7%
-3 4037
 
2.7%
-2 2204
 
1.5%
-1 1382
 
0.9%
0 1209
 
0.8%
-3538 727
 
0.5%
-3536 401
 
0.3%
-0.12 263
 
0.2%
-3535 260
 
0.2%
Other values (61380) 98789
66.6%
(Missing) 7099
 
4.8%
ValueCountFrequency (%)
-3538 727
0.5%
-3536 401
0.3%
-3535 260
 
0.2%
-3533 236
 
0.2%
-3531 173
 
0.1%
-3529 168
 
0.1%
-3528 159
 
0.1%
-3526 144
 
0.1%
-3524 108
 
0.1%
-3523 3
 
< 0.1%
ValueCountFrequency (%)
4385403 1
< 0.1%
4376379 1
< 0.1%
4322411 1
< 0.1%
4258305 1
< 0.1%
4250522 1
< 0.1%
4237973 1
< 0.1%
4232222 1
< 0.1%
4223379 1
< 0.1%
4218294 1
< 0.1%
4201529 1
< 0.1%
2023-05-08T14:16:40.477070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

temperatura
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct334
Distinct (%)0.2%
Missing7056
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean19.779273
Minimum1.6
Maximum37.6
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-05-08T14:16:41.243759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile11.5
Q116.7
median19.5
Q323.1
95-th percentile28.2
Maximum37.6
Range36
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation4.9238106
Coefficient of variation (CV)0.2489379
Kurtosis-0.16249713
Mean19.779273
Median Absolute Deviation (MAD)3.2
Skewness0.0038725063
Sum2792675.1
Variance24.243911
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.611479827 × 10-29
2023-05-08T14:16:41.830468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 1557
 
1.1%
18.8 1555
 
1.0%
19.4 1517
 
1.0%
19.1 1508
 
1.0%
19.3 1504
 
1.0%
18.9 1500
 
1.0%
19 1489
 
1.0%
18.6 1468
 
1.0%
18.7 1452
 
1.0%
19.7 1436
 
1.0%
Other values (324) 126206
85.1%
(Missing) 7056
 
4.8%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
2.1 1
 
< 0.1%
2.5 2
 
< 0.1%
3.2 2
 
< 0.1%
3.4 1
 
< 0.1%
3.5 2
 
< 0.1%
3.6 1
 
< 0.1%
3.7 2
 
< 0.1%
3.8 5
< 0.1%
3.9 5
< 0.1%
ValueCountFrequency (%)
37.6 1
< 0.1%
36.8 1
< 0.1%
36.7 1
< 0.1%
36.5 1
< 0.1%
36.4 1
< 0.1%
36.3 1
< 0.1%
36.2 1
< 0.1%
35.8 2
< 0.1%
35.7 2
< 0.1%
35.6 1
< 0.1%
2023-05-08T14:16:43.341601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

temperatura_orvalho
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct265
Distinct (%)0.2%
Missing7285
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean14.365055
Minimum-6.3
Maximum25.6
Zeros3
Zeros (%)< 0.1%
Memory size1.1 MiB
2023-05-08T14:16:43.751215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-6.3
5-th percentile7.7
Q111.9
median15
Q317.3
95-th percentile19.1
Maximum25.6
Range31.9
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation3.658414
Coefficient of variation (CV)0.25467456
Kurtosis0.043476628
Mean14.365055
Median Absolute Deviation (MAD)2.6
Skewness-0.69845588
Sum2024941.2
Variance13.383993
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.486805007 × 10-27
2023-05-08T14:16:44.048313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 1860
 
1.3%
17.1 1831
 
1.2%
17.9 1828
 
1.2%
17.4 1825
 
1.2%
17.5 1821
 
1.2%
17.6 1820
 
1.2%
17.8 1820
 
1.2%
17.3 1784
 
1.2%
17 1779
 
1.2%
17.2 1774
 
1.2%
Other values (255) 122821
82.8%
(Missing) 7285
 
4.9%
ValueCountFrequency (%)
-6.3 1
< 0.1%
-6.1 1
< 0.1%
-5.9 2
< 0.1%
-5.5 2
< 0.1%
-5.3 1
< 0.1%
-5.1 1
< 0.1%
-4.9 2
< 0.1%
-4.7 1
< 0.1%
-4.3 2
< 0.1%
-3.7 2
< 0.1%
ValueCountFrequency (%)
25.6 1
 
< 0.1%
24.6 1
 
< 0.1%
23 1
 
< 0.1%
22.6 1
 
< 0.1%
22.5 2
 
< 0.1%
22.4 2
 
< 0.1%
22.2 2
 
< 0.1%
22.1 5
< 0.1%
22 4
< 0.1%
21.9 5
< 0.1%
2023-05-08T14:16:45.440442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

temp_max_hr_anterior
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct338
Distinct (%)0.2%
Missing7470
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean20.51523
Minimum2.1
Maximum37.8
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-05-08T14:16:45.848233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile12.1
Q117.3
median20.1
Q324.1
95-th percentile29.2
Maximum37.8
Range35.7
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation5.1140667
Coefficient of variation (CV)0.24928147
Kurtosis-0.29493851
Mean20.51523
Median Absolute Deviation (MAD)3.4
Skewness0.039513306
Sum2888093
Variance26.153678
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.261188546 × 10-29
2023-05-08T14:16:46.179797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 1525
 
1.0%
19.1 1437
 
1.0%
19.5 1430
 
1.0%
19.3 1407
 
0.9%
19 1403
 
0.9%
19.4 1389
 
0.9%
18.9 1376
 
0.9%
18.8 1376
 
0.9%
19.7 1367
 
0.9%
19.9 1364
 
0.9%
Other values (328) 126704
85.5%
(Missing) 7470
 
5.0%
ValueCountFrequency (%)
2.1 1
 
< 0.1%
2.5 1
 
< 0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.8 2
< 0.1%
3.9 4
< 0.1%
4 1
 
< 0.1%
4.1 1
 
< 0.1%
4.3 1
 
< 0.1%
4.4 2
< 0.1%
ValueCountFrequency (%)
37.8 1
< 0.1%
37.6 1
< 0.1%
37.4 1
< 0.1%
37.2 1
< 0.1%
37 1
< 0.1%
36.9 1
< 0.1%
36.7 2
< 0.1%
36.6 1
< 0.1%
36.4 2
< 0.1%
36.3 1
< 0.1%
2023-05-08T14:16:47.659402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

temp_min_hr_anterior
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct322
Distinct (%)0.2%
Missing7470
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean19.08855
Minimum1.4
Maximum36.2
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-05-08T14:16:48.074771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile11
Q116.2
median19
Q322.1
95-th percentile27.1
Maximum36.2
Range34.8
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation4.7489778
Coefficient of variation (CV)0.24878672
Kurtosis-0.068652947
Mean19.08855
Median Absolute Deviation (MAD)3
Skewness-0.055497642
Sum2687247.9
Variance22.55279
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.992739738 × 10-28
2023-05-08T14:16:48.397743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 1645
 
1.1%
18.7 1637
 
1.1%
19 1590
 
1.1%
19.1 1588
 
1.1%
18.8 1588
 
1.1%
18.6 1576
 
1.1%
18.9 1573
 
1.1%
18.4 1525
 
1.0%
19.3 1518
 
1.0%
19.4 1515
 
1.0%
Other values (312) 125023
84.3%
(Missing) 7470
 
5.0%
ValueCountFrequency (%)
1.4 2
< 0.1%
1.9 1
 
< 0.1%
2.5 4
< 0.1%
2.6 2
< 0.1%
2.9 1
 
< 0.1%
3.1 1
 
< 0.1%
3.2 2
< 0.1%
3.4 1
 
< 0.1%
3.5 4
< 0.1%
3.6 2
< 0.1%
ValueCountFrequency (%)
36.2 1
 
< 0.1%
35.4 3
< 0.1%
35 1
 
< 0.1%
34.8 1
 
< 0.1%
34.7 1
 
< 0.1%
34.6 2
< 0.1%
34.5 1
 
< 0.1%
34.3 4
< 0.1%
34.2 1
 
< 0.1%
33.9 1
 
< 0.1%
2023-05-08T14:16:49.846579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

umidade_max_hr_anterior
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct89
Distinct (%)0.1%
Missing7710
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean77.607501
Minimum12
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-05-08T14:16:50.284480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile41
Q165
median84
Q393
95-th percentile97
Maximum100
Range88
Interquartile range (IQR)28

Descriptive statistics

Standard deviation18.409207
Coefficient of variation (CV)0.23720912
Kurtosis-0.096974941
Mean77.607501
Median Absolute Deviation (MAD)11
Skewness-0.92742198
Sum10906803
Variance338.8989
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-05-08T14:16:50.594947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 7879
 
5.3%
95 7333
 
4.9%
94 6526
 
4.4%
97 5873
 
4.0%
93 5852
 
3.9%
92 5666
 
3.8%
91 4765
 
3.2%
90 4223
 
2.8%
89 3925
 
2.6%
88 3667
 
2.5%
Other values (79) 84829
57.2%
(Missing) 7710
 
5.2%
ValueCountFrequency (%)
12 1
 
< 0.1%
13 3
 
< 0.1%
14 7
 
< 0.1%
15 4
 
< 0.1%
16 4
 
< 0.1%
17 19
 
< 0.1%
18 41
< 0.1%
19 59
< 0.1%
20 63
< 0.1%
21 85
0.1%
ValueCountFrequency (%)
100 763
 
0.5%
99 619
 
0.4%
98 2525
 
1.7%
97 5873
4.0%
96 7879
5.3%
95 7333
4.9%
94 6526
4.4%
93 5852
3.9%
92 5666
3.8%
91 4765
3.2%
2023-05-08T14:16:52.010806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

umidade_min_hr_anterior
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct91
Distinct (%)0.1%
Missing7710
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean71.164375
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-05-08T14:16:52.657779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile33
Q155
median76
Q389
95-th percentile96
Maximum100
Range90
Interquartile range (IQR)34

Descriptive statistics

Standard deviation20.584654
Coefficient of variation (CV)0.28925503
Kurtosis-0.74895319
Mean71.164375
Median Absolute Deviation (MAD)15
Skewness-0.60309458
Sum10001299
Variance423.72797
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-05-08T14:16:53.255748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92 4284
 
2.9%
94 4182
 
2.8%
93 4092
 
2.8%
91 4042
 
2.7%
95 4012
 
2.7%
90 3896
 
2.6%
96 3825
 
2.6%
89 3667
 
2.5%
88 3531
 
2.4%
87 3407
 
2.3%
Other values (81) 101600
68.5%
(Missing) 7710
 
5.2%
ValueCountFrequency (%)
10 6
 
< 0.1%
11 4
 
< 0.1%
12 11
 
< 0.1%
13 16
 
< 0.1%
14 37
 
< 0.1%
15 62
< 0.1%
16 72
< 0.1%
17 117
0.1%
18 115
0.1%
19 143
0.1%
ValueCountFrequency (%)
100 358
 
0.2%
99 329
 
0.2%
98 1420
 
1.0%
97 3033
2.0%
96 3825
2.6%
95 4012
2.7%
94 4182
2.8%
93 4092
2.8%
92 4284
2.9%
91 4042
2.7%
2023-05-08T14:16:56.487212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

umidade
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct90
Distinct (%)0.1%
Missing7284
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean74.412027
Minimum11
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.1 MiB
2023-05-08T14:16:57.230005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile37
Q160
median80
Q391
95-th percentile97
Maximum100
Range89
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.634934
Coefficient of variation (CV)0.26386775
Kurtosis-0.47736995
Mean74.412027
Median Absolute Deviation (MAD)13
Skewness-0.75540489
Sum10489417
Variance385.53064
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-05-08T14:16:57.821961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 5698
 
3.8%
95 5662
 
3.8%
94 5341
 
3.6%
92 5042
 
3.4%
93 5014
 
3.4%
91 4431
 
3.0%
97 4320
 
2.9%
90 4104
 
2.8%
89 3809
 
2.6%
88 3669
 
2.5%
Other values (80) 93874
63.3%
(Missing) 7284
 
4.9%
ValueCountFrequency (%)
11 2
 
< 0.1%
12 5
 
< 0.1%
13 11
 
< 0.1%
14 9
 
< 0.1%
15 18
 
< 0.1%
16 34
 
< 0.1%
17 77
0.1%
18 75
0.1%
19 92
0.1%
20 107
0.1%
ValueCountFrequency (%)
100 552
 
0.4%
99 461
 
0.3%
98 1909
 
1.3%
97 4320
2.9%
96 5698
3.8%
95 5662
3.8%
94 5341
3.6%
93 5014
3.4%
92 5042
3.4%
91 4431
3.0%
2023-05-08T14:16:59.416804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

vento_direcao
Real number (ℝ)

Distinct360
Distinct (%)0.3%
Missing7442
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean151.34381
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-08T14:16:59.845735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22
Q187
median134
Q3193
95-th percentile337
Maximum360
Range359
Interquartile range (IQR)106

Descriptive statistics

Standard deviation94.082834
Coefficient of variation (CV)0.62164971
Kurtosis-0.45672998
Mean151.34381
Median Absolute Deviation (MAD)52
Skewness0.6594246
Sum21310116
Variance8851.5796
MonotonicityNot monotonic
2023-05-08T14:17:00.099131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 1406
 
0.9%
136 1388
 
0.9%
134 1349
 
0.9%
131 1329
 
0.9%
137 1327
 
0.9%
139 1318
 
0.9%
130 1316
 
0.9%
133 1309
 
0.9%
140 1303
 
0.9%
135 1296
 
0.9%
Other values (350) 127465
86.0%
(Missing) 7442
 
5.0%
ValueCountFrequency (%)
1 270
0.2%
2 296
0.2%
3 273
0.2%
4 286
0.2%
5 331
0.2%
6 277
0.2%
7 300
0.2%
8 276
0.2%
9 325
0.2%
10 301
0.2%
ValueCountFrequency (%)
360 262
0.2%
359 269
0.2%
358 290
0.2%
357 281
0.2%
356 275
0.2%
355 263
0.2%
354 275
0.2%
353 293
0.2%
352 305
0.2%
351 278
0.2%

vento_velocidade
Real number (ℝ)

MISSING  ZEROS 

Distinct132
Distinct (%)0.1%
Missing7442
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean2.4001285
Minimum0
Maximum15.5
Zeros3827
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-08T14:17:00.419333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q11.2
median2.1
Q33.3
95-th percentile5.3
Maximum15.5
Range15.5
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.5862629
Coefficient of variation (CV)0.66090746
Kurtosis1.3781604
Mean2.4001285
Median Absolute Deviation (MAD)1
Skewness0.94531105
Sum337952.5
Variance2.5162299
MonotonicityNot monotonic
2023-05-08T14:17:00.702736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 4107
 
2.8%
1.4 4081
 
2.8%
1.3 4064
 
2.7%
1.7 3999
 
2.7%
1.6 3995
 
2.7%
1.2 3949
 
2.7%
1.8 3920
 
2.6%
0 3827
 
2.6%
1.9 3802
 
2.6%
2 3789
 
2.6%
Other values (122) 101273
68.3%
(Missing) 7442
 
5.0%
ValueCountFrequency (%)
0 3827
2.6%
0.1 1916
1.3%
0.2 1829
1.2%
0.3 2010
1.4%
0.4 1985
1.3%
0.5 2202
1.5%
0.6 2296
1.5%
0.7 2603
1.8%
0.8 2764
1.9%
0.9 3219
2.2%
ValueCountFrequency (%)
15.5 1
 
< 0.1%
14.8 1
 
< 0.1%
14.6 1
 
< 0.1%
14.3 2
< 0.1%
13.3 1
 
< 0.1%
13.2 2
< 0.1%
12.8 2
< 0.1%
12.7 3
< 0.1%
12.5 3
< 0.1%
12.2 3
< 0.1%

data_hora
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct148248
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.7 MiB
2006-06-08 00:00:00
 
1
2017-09-18 00:00:00
 
1
2017-09-15 20:00:00
 
1
2017-09-15 21:00:00
 
1
2017-09-15 22:00:00
 
1
Other values (148243)
148243 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters2816712
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148248 ?
Unique (%)100.0%

Sample

1st row2006-06-08 00:00:00
2nd row2006-06-08 01:00:00
3rd row2006-06-08 02:00:00
4th row2006-06-08 03:00:00
5th row2006-06-08 04:00:00

Common Values

ValueCountFrequency (%)
2006-06-08 00:00:00 1
 
< 0.1%
2017-09-18 00:00:00 1
 
< 0.1%
2017-09-15 20:00:00 1
 
< 0.1%
2017-09-15 21:00:00 1
 
< 0.1%
2017-09-15 22:00:00 1
 
< 0.1%
2017-09-15 23:00:00 1
 
< 0.1%
2017-09-16 00:00:00 1
 
< 0.1%
2017-09-16 01:00:00 1
 
< 0.1%
2017-09-16 02:00:00 1
 
< 0.1%
2017-09-16 03:00:00 1
 
< 0.1%
Other values (148238) 148238
> 99.9%

Length

2023-05-08T14:17:00.959988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00:00 6177
 
2.1%
06:00:00 6177
 
2.1%
15:00:00 6177
 
2.1%
14:00:00 6177
 
2.1%
13:00:00 6177
 
2.1%
11:00:00 6177
 
2.1%
16:00:00 6177
 
2.1%
17:00:00 6177
 
2.1%
18:00:00 6177
 
2.1%
19:00:00 6177
 
2.1%
Other values (6191) 234726
79.2%

Most occurring characters

ValueCountFrequency (%)
0 1051749
37.3%
2 325551
 
11.6%
1 314061
 
11.1%
- 296496
 
10.5%
: 296496
 
10.5%
148248
 
5.3%
3 64875
 
2.3%
8 57162
 
2.0%
7 57114
 
2.0%
9 56418
 
2.0%
Other values (3) 148542
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2075472
73.7%
Dash Punctuation 296496
 
10.5%
Other Punctuation 296496
 
10.5%
Space Separator 148248
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1051749
50.7%
2 325551
 
15.7%
1 314061
 
15.1%
3 64875
 
3.1%
8 57162
 
2.8%
7 57114
 
2.8%
9 56418
 
2.7%
6 52794
 
2.5%
4 47970
 
2.3%
5 47778
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 296496
100.0%
Other Punctuation
ValueCountFrequency (%)
: 296496
100.0%
Space Separator
ValueCountFrequency (%)
148248
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2816712
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1051749
37.3%
2 325551
 
11.6%
1 314061
 
11.1%
- 296496
 
10.5%
: 296496
 
10.5%
148248
 
5.3%
3 64875
 
2.3%
8 57162
 
2.0%
7 57114
 
2.0%
9 56418
 
2.0%
Other values (3) 148542
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2816712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1051749
37.3%
2 325551
 
11.6%
1 314061
 
11.1%
- 296496
 
10.5%
: 296496
 
10.5%
148248
 
5.3%
3 64875
 
2.3%
8 57162
 
2.0%
7 57114
 
2.0%
9 56418
 
2.0%
Other values (3) 148542
 
5.3%

time
Numeric time series

UNIFORM  UNIQUE 

Distinct148248
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74123.5
Minimum0
Maximum148247
Zeros1
Zeros (%)< 0.1%
Memory size1.1 MiB
2023-05-08T14:17:01.170913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7412.35
Q137061.75
median74123.5
Q3111185.25
95-th percentile140834.65
Maximum148247
Range148247
Interquartile range (IQR)74123.5

Descriptive statistics

Standard deviation42795.656
Coefficient of variation (CV)0.57735611
Kurtosis-1.2
Mean74123.5
Median Absolute Deviation (MAD)37062
Skewness0
Sum1.0988661 × 1010
Variance1.8314681 × 109
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0
2023-05-08T14:17:01.451342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
98880 1
 
< 0.1%
98828 1
 
< 0.1%
98829 1
 
< 0.1%
98830 1
 
< 0.1%
98831 1
 
< 0.1%
98832 1
 
< 0.1%
98833 1
 
< 0.1%
98834 1
 
< 0.1%
98835 1
 
< 0.1%
Other values (148238) 148238
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
148247 1
< 0.1%
148246 1
< 0.1%
148245 1
< 0.1%
148244 1
< 0.1%
148243 1
< 0.1%
148242 1
< 0.1%
148241 1
< 0.1%
148240 1
< 0.1%
148239 1
< 0.1%
148238 1
< 0.1%
2023-05-08T14:17:02.943285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

estacao
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
inverno
38352 
verão
36816 
outono
36768 
primavera
36312 

Length

Max length9
Median length7
Mean length6.7451837
Min length5

Characters and Unicode

Total characters999960
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoutono
2nd rowoutono
3rd rowoutono
4th rowoutono
5th rowoutono

Common Values

ValueCountFrequency (%)
inverno 38352
25.9%
verão 36816
24.8%
outono 36768
24.8%
primavera 36312
24.5%

Length

2023-05-08T14:17:03.375048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-08T14:17:03.648413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
inverno 38352
25.9%
verão 36816
24.8%
outono 36768
24.8%
primavera 36312
24.5%

Most occurring characters

ValueCountFrequency (%)
o 185472
18.5%
r 147792
14.8%
n 113472
11.3%
v 111480
11.1%
e 111480
11.1%
i 74664
7.5%
a 72624
 
7.3%
ã 36816
 
3.7%
u 36768
 
3.7%
t 36768
 
3.7%
Other values (2) 72624
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 999960
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 185472
18.5%
r 147792
14.8%
n 113472
11.3%
v 111480
11.1%
e 111480
11.1%
i 74664
7.5%
a 72624
 
7.3%
ã 36816
 
3.7%
u 36768
 
3.7%
t 36768
 
3.7%
Other values (2) 72624
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 999960
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 185472
18.5%
r 147792
14.8%
n 113472
11.3%
v 111480
11.1%
e 111480
11.1%
i 74664
7.5%
a 72624
 
7.3%
ã 36816
 
3.7%
u 36768
 
3.7%
t 36768
 
3.7%
Other values (2) 72624
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 963144
96.3%
None 36816
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 185472
19.3%
r 147792
15.3%
n 113472
11.8%
v 111480
11.6%
e 111480
11.6%
i 74664
7.8%
a 72624
 
7.5%
u 36768
 
3.8%
t 36768
 
3.8%
p 36312
 
3.8%
None
ValueCountFrequency (%)
ã 36816
100.0%

Interactions

2023-05-08T14:16:28.036328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:41.195926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:44.736104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:48.654366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:53.476429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:56.776272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:00.249002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:03.760683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:08.958350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:12.190031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:15.669788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:18.815146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:23.552193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:28.302449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:41.483348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:44.987144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:49.053837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:53.729970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:57.027857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:00.507936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:04.149130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:09.230456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:12.747953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:15.924493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:19.102283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:23.935476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:28.547112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:41.722676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:45.212916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:49.410635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:53.994495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:57.280191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:00.730943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:04.525333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:09.467878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:12.969413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:16.144272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:19.387652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:24.363261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:28.783612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:42.130952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:45.468631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:49.796107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:54.237110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:57.524223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:00.974102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:04.878422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:09.735342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:13.212380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:16.376030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:19.796367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:24.775633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:29.036198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:42.398287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:45.727842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:50.184182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:54.493263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:57.774472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:01.244799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:05.292124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:09.983923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:13.470234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:16.624178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:20.178653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:25.204764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:29.302868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:42.676727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:45.972863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:50.824623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:54.745706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:58.009754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:01.486883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:05.684202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:10.224458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:13.719389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:16.877803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:20.559253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:25.561467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:29.587082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:42.932479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:46.223564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:51.244372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:55.004460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:58.273190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:01.742952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:06.088289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:10.462836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:13.961698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:17.140354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:20.973403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:25.814693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:29.846812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:43.191009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:46.529777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:51.587146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:55.259192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:58.525989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:01.989870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:06.466372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:10.719163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:14.206755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:17.390039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:21.391493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:26.087079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:30.099262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:43.450649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:46.886747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:51.974060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:55.507863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:58.755198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:02.234415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:06.847089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:10.941896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:14.429630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:17.619214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:21.782705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:26.344358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:30.362725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:43.707651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:47.216747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:52.369225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:55.755286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:58.980097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:02.466503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:07.250001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:11.178008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:14.651068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:17.849255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:22.139502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:26.612163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:30.598318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:43.942324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:47.574913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:52.711035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:55.985124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:59.247448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:02.708490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:07.630163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:11.410169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:14.894436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:18.071250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:22.480183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:26.868329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:30.850620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:44.203968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:47.920679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:52.970866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:56.261051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:59.499031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:03.001858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:08.064503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:11.683554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:15.146091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:18.313152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:22.815326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:27.126918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:31.106799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:44.464256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:48.282114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:53.220071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:56.523440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:15:59.993850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:03.386453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:08.518394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:11.938657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:15.418555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:18.567689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:23.146758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-08T14:16:27.407044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-08T14:17:03.858507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
chuvapressaoradiacaotemperaturatemperatura_orvalhotemp_max_hr_anteriortemp_min_hr_anteriorumidade_max_hr_anteriorumidade_min_hr_anteriorumidadevento_direcaovento_velocidadetimeestacao
chuva1.000-0.182-0.044-0.0620.258-0.052-0.0420.2940.2500.2950.0400.0110.0050.032
pressao-0.1821.000-0.046-0.441-0.567-0.434-0.4880.0250.0300.008-0.1550.0050.0130.356
radiacao-0.044-0.0461.0000.561-0.0190.5390.475-0.454-0.519-0.549-0.0380.2370.0830.073
temperatura-0.062-0.4410.5611.0000.3450.9870.979-0.687-0.715-0.7210.0010.2290.0400.261
temperatura_orvalho0.258-0.567-0.0190.3451.0000.3100.3680.3080.2860.2970.042-0.0650.0040.415
temp_max_hr_anterior-0.052-0.4340.5390.9870.3101.0000.976-0.721-0.758-0.7420.0110.2450.0370.252
temp_min_hr_anterior-0.042-0.4880.4750.9790.3680.9761.000-0.680-0.683-0.6770.0150.2410.0450.274
umidade_max_hr_anterior0.2940.025-0.454-0.6870.308-0.721-0.6801.0000.9660.9680.050-0.348-0.0540.139
umidade_min_hr_anterior0.2500.030-0.519-0.7150.286-0.758-0.6830.9661.0000.9810.036-0.329-0.0450.136
umidade0.2950.008-0.549-0.7210.297-0.742-0.6770.9680.9811.0000.058-0.333-0.0500.136
vento_direcao0.040-0.155-0.0380.0010.0420.0110.0150.0500.0360.0581.000-0.0070.0010.092
vento_velocidade0.0110.0050.2370.229-0.0650.2450.241-0.348-0.329-0.333-0.0071.000-0.0380.080
time0.0050.0130.0830.0400.0040.0370.045-0.054-0.045-0.0500.001-0.0381.0000.122
estacao0.0320.3560.0730.2610.4150.2520.2740.1390.1360.1360.0920.0800.1221.000

Missing values

2023-05-08T14:16:31.549483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-08T14:16:32.208222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-08T14:16:33.055808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

chuvapressaoradiacaotemperaturatemperatura_orvalhotemp_max_hr_anteriortemp_min_hr_anteriorumidade_max_hr_anteriorumidade_min_hr_anteriorumidadevento_direcaovento_velocidadedata_horatimeestacao
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2006-06-08 00:00:000outono
1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2006-06-08 01:00:001outono
2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2006-06-08 02:00:002outono
3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2006-06-08 03:00:003outono
4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2006-06-08 04:00:004outono
5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2006-06-08 05:00:005outono
6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2006-06-08 06:00:006outono
7NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2006-06-08 07:00:007outono
8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2006-06-08 08:00:008outono
9NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2006-06-08 09:00:009outono
chuvapressaoradiacaotemperaturatemperatura_orvalhotemp_max_hr_anteriortemp_min_hr_anteriorumidade_max_hr_anteriorumidade_min_hr_anteriorumidadevento_direcaovento_velocidadedata_horatimeestacao
148238NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-05-06 14:00:00148238outono
148239NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-05-06 15:00:00148239outono
148240NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-05-06 16:00:00148240outono
148241NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-05-06 17:00:00148241outono
148242NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-05-06 18:00:00148242outono
148243NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-05-06 19:00:00148243outono
148244NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-05-06 20:00:00148244outono
148245NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-05-06 21:00:00148245outono
148246NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-05-06 22:00:00148246outono
148247NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2023-05-06 23:00:00148247outono